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Demand Response Analysis Framework (DRAF): An Open-Source Multi-Objective Decision Support Tool for Decarbonizing Local Multi-Energy Systems

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  • Markus Fleschutz

    (Department of Process, Energy and Transport Engineering, Munster Technological University, T12 P928 Cork, Ireland
    Institute of Refrigeration, Air-Conditioning, and Environmental Engineering, Karlsruhe University of Applied Sciences, Moltkestraße 30, 76133 Karlsruhe, Germany)

  • Markus Bohlayer

    (Institute of Refrigeration, Air-Conditioning, and Environmental Engineering, Karlsruhe University of Applied Sciences, Moltkestraße 30, 76133 Karlsruhe, Germany)

  • Marco Braun

    (Institute of Refrigeration, Air-Conditioning, and Environmental Engineering, Karlsruhe University of Applied Sciences, Moltkestraße 30, 76133 Karlsruhe, Germany)

  • Michael D. Murphy

    (Department of Process, Energy and Transport Engineering, Munster Technological University, T12 P928 Cork, Ireland)

Abstract

A major barrier to investments in clean and future-proof energy technologies of local multi-energy systems (L-MESs) is the lack of knowledge about their impacts on profitability and carbon footprints due to their complex techno-economic interactions. To reduce this problem, decision support tools should integrate various forms of decarbonization measures. This paper proposes the Demand Response Analysis Framework (DRAF), a new open-source Python decision support tool that integrally optimizes the design and operation of energy technologies considering demand-side flexibility, electrification, and renewable energy sources. It quantifies decarbonization and cost reduction potential using multi-objective mixed-integer linear programming and provides decision-makers of L-MESs with optimal scenarios regarding costs, emissions, or Pareto efficiency. DRAF supports all steps of the energy system optimization process from time series analysis to interactive plotting and data export. It comes with several component templates that allow a quick start without limiting the modeling possibilities thanks to a generic model generator. Other key features are the access and preparation of time series, such as dynamic carbon emission factors or wholesale electricity prices; and the generation, handling, and parallel computing of scenarios. We demonstrate DRAF’s capabilities through three case studies on (1) the DR of industrial production processes, (2) the design optimization of battery and photovoltaic systems, and (3) the design optimization and DR of distributed thermal energy resources.

Suggested Citation

  • Markus Fleschutz & Markus Bohlayer & Marco Braun & Michael D. Murphy, 2022. "Demand Response Analysis Framework (DRAF): An Open-Source Multi-Objective Decision Support Tool for Decarbonizing Local Multi-Energy Systems," Sustainability, MDPI, vol. 14(13), pages 1-38, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:8025-:d:852935
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    1. Markus Fleschutz & Markus Bohlayer & Marco Braun & Michael D. Murphy, 2023. "From prosumer to flexumer: Case study on the value of flexibility in decarbonizing the multi-energy system of a manufacturing company," Papers 2301.07997, arXiv.org.
    2. Leippi, Andre & Fleschutz, Markus & Davis, Kevin & Klingler, Anna-Lena & Murphy, Michael D., 2024. "Optimizing electric vehicle fleet integration in industrial demand response: Maximizing vehicle-to-grid benefits while compensating vehicle owners for battery degradation," Applied Energy, Elsevier, vol. 374(C).

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